| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 29 | | tagDensity | 0.207 | | leniency | 0.414 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 90.38% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1040 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 56.73% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1040 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "echoed" | | 1 | "chill" | | 2 | "perfect" | | 3 | "scanning" | | 4 | "etched" | | 5 | "intricate" | | 6 | "pawn" | | 7 | "tracing" | | 8 | "sense of" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 77 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 77 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 99 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1034 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 86.53% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 21 | | wordCount | 709 | | uniqueNames | 7 | | maxNameDensity | 1.27 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Harlow | 1 | | Quinn | 9 | | Detective | 2 | | Sergeant | 1 | | Finch | 6 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Sergeant" | | 3 | "Finch" | | 4 | "Morris" |
| | places | (empty) | | globalScore | 0.865 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 54 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1034 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 99 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 29.54 | | std | 22.97 | | cv | 0.777 | | sampleLengths | | 0 | 72 | | 1 | 2 | | 2 | 48 | | 3 | 21 | | 4 | 8 | | 5 | 3 | | 6 | 26 | | 7 | 57 | | 8 | 30 | | 9 | 28 | | 10 | 64 | | 11 | 3 | | 12 | 13 | | 13 | 9 | | 14 | 34 | | 15 | 24 | | 16 | 9 | | 17 | 45 | | 18 | 48 | | 19 | 9 | | 20 | 5 | | 21 | 15 | | 22 | 41 | | 23 | 43 | | 24 | 54 | | 25 | 42 | | 26 | 70 | | 27 | 1 | | 28 | 14 | | 29 | 6 | | 30 | 13 | | 31 | 62 | | 32 | 23 | | 33 | 11 | | 34 | 81 |
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| 96.15% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 77 | | matches | | 0 | "was stained" | | 1 | "was etched" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 114 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 99 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 713 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.029453015427769985 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.009817671809256662 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 99 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 99 | | mean | 10.44 | | std | 7.31 | | cv | 0.7 | | sampleLengths | | 0 | 22 | | 1 | 22 | | 2 | 14 | | 3 | 14 | | 4 | 2 | | 5 | 7 | | 6 | 15 | | 7 | 10 | | 8 | 16 | | 9 | 13 | | 10 | 8 | | 11 | 8 | | 12 | 3 | | 13 | 26 | | 14 | 3 | | 15 | 20 | | 16 | 11 | | 17 | 9 | | 18 | 6 | | 19 | 5 | | 20 | 3 | | 21 | 11 | | 22 | 5 | | 23 | 3 | | 24 | 11 | | 25 | 16 | | 26 | 12 | | 27 | 6 | | 28 | 8 | | 29 | 10 | | 30 | 13 | | 31 | 4 | | 32 | 4 | | 33 | 19 | | 34 | 3 | | 35 | 3 | | 36 | 10 | | 37 | 9 | | 38 | 7 | | 39 | 27 | | 40 | 20 | | 41 | 4 | | 42 | 9 | | 43 | 4 | | 44 | 21 | | 45 | 6 | | 46 | 14 | | 47 | 11 | | 48 | 9 | | 49 | 12 |
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| 63.97% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.41414141414141414 | | totalSentences | 99 | | uniqueOpeners | 41 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 71 | | matches | (empty) | | ratio | 0 | |
| 79.15% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 71 | | matches | | 0 | "Her colleague, Detective Sergeant Finch," | | 1 | "He offered a tight smile," | | 2 | "She stepped past the tape," | | 3 | "She circled the body slowly," | | 4 | "His hands were clean, unblemished." | | 5 | "She knelt, her sharp jaw" | | 6 | "It was like ozone after" | | 7 | "It was on the floor" | | 8 | "She leaned closer, her breath" | | 9 | "It wasn’t a pool." | | 10 | "It was a circle." | | 11 | "He peered down." | | 12 | "She stood, her eyes scanning" | | 13 | "She walked the perimeter of" | | 14 | "She retrieved a pair of" | | 15 | "It was a small brass" | | 16 | "It spun lazily, then stopped," | | 17 | "She walked toward the break," | | 18 | "She crouched again, tracing the" | | 19 | "Her mind flashed back three" |
| | ratio | 0.352 | |
| 9.30% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 64 | | totalSentences | 71 | | matches | | 0 | "The air in the abandoned" | | 1 | "Detective Harlow Quinn descended the" | | 2 | "The sound of her boots" | | 3 | "A uniformed officer stood by" | | 4 | "Quinn nodded, her gaze sweeping" | | 5 | "The curved walls of the" | | 6 | "A man, late thirties, dressed" | | 7 | "Her colleague, Detective Sergeant Finch," | | 8 | "He offered a tight smile," | | 9 | "Quinn didn’t reply." | | 10 | "She stepped past the tape," | | 11 | "She circled the body slowly," | | 12 | "Thorne’s eyes were wide, fixed" | | 13 | "His hands were clean, unblemished." | | 14 | "She knelt, her sharp jaw" | | 15 | "The smell was stronger here." | | 16 | "It was like ozone after" | | 17 | "Finch said, gesturing with his" | | 18 | "Quinn’s focus wasn’t on the" | | 19 | "It was on the floor" |
| | ratio | 0.901 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 71 | | matches | (empty) | | ratio | 0 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 3 | | matches | | 0 | "She stepped past the tape, her movements carrying a military precision that made the uniformed officer stand a little straighter." | | 1 | "A near-perfect ring of blood that enclosed the victim, with only a single, clean break on the western side." | | 2 | "She’d seen something in the flickering darkness before the blast, something that defied every law of physics she held dear." |
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| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 1 | | matches | | 0 | "She stood, her eyes scanning the darkness beyond the work lights" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | 0 | "Quinn murmured (murmur)" |
| | dialogueSentences | 29 | | tagDensity | 0.103 | | leniency | 0.207 | | rawRatio | 0.333 | | effectiveRatio | 0.069 | |